基于小波分析的MT静态效应识别及校正

Recognition and correction of static shift for MT based on wavelets analysis

大地电磁测深中,观测结果常受静态效应的影响而发生畸变,增加了资料解释工作的难度。基于小波分析理论,提出使用Daubechies小波分解对有静态效应测点进行有效识别。建立含有静态体并镶嵌有高阻异常体的地垒模型,分别采用中值滤波方法、相位法和小波分析方法进行了静态效应的校正。结果表明:相位法校正结果不稳定,相对误差随频点起伏较大;中值滤波方法在高频段误差较大;基于多尺度小波分析校正方法比较稳定,误差基本不超过5%,能够有效的压制静态效应。采用小波分析方法对某区大地电磁(MT)实测数据进行了静态效应识别和校正,取得比较理想的效果。

In magnetotelluric sounding, the observed electromagnetic field is often distorted because of static shift, increasing the difficulty of data interpretation. In this paper, Daubechies wavelet decomposition method is proposed to recognize measuring point with static shift based on the theory of wavelet analysis. Horst model with inhomogeneous bodies is established and static shift is removed using median filtering method, phase method and wavelet analysis method respectively. The results show that the phase correction result is not stable, relative error has great fluctuation with the frequency; relative error is larger in high frequency than low one for median filtering method, multi scale wavelet analysis correction method is stable, and relative error is not more than 5%, which can effectively suppress the static shift. Finally, static shift is recognized and corrected using the wavelet analysis method for magnetotelluric （ MT ） data in an area and better results are achieved.